Overview

Dataset statistics

Number of variables42
Number of observations10000
Missing cells281
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory336.0 B

Variable types

Numeric29
DateTime2
Categorical11

Alerts

MOBILENO_AVL_FLAG has constant value "1" Constant
AVERAGE_ACCT_AGE has a high cardinality: 112 distinct values High cardinality
CREDIT_HISTORY_LENGTH has a high cardinality: 187 distinct values High cardinality
DISBURSED_AMOUNT is highly correlated with ASSET_COSTHigh correlation
ASSET_COST is highly correlated with DISBURSED_AMOUNTHigh correlation
AADHAR_FLAG is highly correlated with VOTERID_FLAGHigh correlation
VOTERID_FLAG is highly correlated with AADHAR_FLAGHigh correlation
PERFORM_CNS_SCORE is highly correlated with PRI_NO_OF_ACCTS and 5 other fieldsHigh correlation
PRI_NO_OF_ACCTS is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_ACTIVE_ACCTS is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_CURRENT_BALANCE is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_SANCTIONED_AMOUNT is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_DISBURSED_AMOUNT is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
SEC_NO_OF_ACCTS is highly correlated with SEC_ACTIVE_ACCTS and 5 other fieldsHigh correlation
SEC_ACTIVE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
SEC_OVERDUE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 4 other fieldsHigh correlation
SEC_CURRENT_BALANCE is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
SEC_SANCTIONED_AMOUNT is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
SEC_DISBURSED_AMOUNT is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
PRIMARY_INSTAL_AMT is highly correlated with PERFORM_CNS_SCORE and 5 other fieldsHigh correlation
SEC_INSTAL_AMT is highly correlated with SEC_NO_OF_ACCTS and 4 other fieldsHigh correlation
NEW_ACCTS_IN_LAST_SIX_MONTHS is highly correlated with PRI_NO_OF_ACCTS and 4 other fieldsHigh correlation
DISBURSED_AMOUNT is highly correlated with ASSET_COSTHigh correlation
ASSET_COST is highly correlated with DISBURSED_AMOUNTHigh correlation
AADHAR_FLAG is highly correlated with VOTERID_FLAGHigh correlation
VOTERID_FLAG is highly correlated with AADHAR_FLAGHigh correlation
PRI_NO_OF_ACCTS is highly correlated with PRI_ACTIVE_ACCTS and 1 other fieldsHigh correlation
PRI_ACTIVE_ACCTS is highly correlated with PRI_NO_OF_ACCTS and 1 other fieldsHigh correlation
PRI_CURRENT_BALANCE is highly correlated with PRI_SANCTIONED_AMOUNT and 1 other fieldsHigh correlation
PRI_SANCTIONED_AMOUNT is highly correlated with PRI_CURRENT_BALANCE and 1 other fieldsHigh correlation
PRI_DISBURSED_AMOUNT is highly correlated with PRI_CURRENT_BALANCE and 1 other fieldsHigh correlation
SEC_NO_OF_ACCTS is highly correlated with SEC_ACTIVE_ACCTS and 1 other fieldsHigh correlation
SEC_ACTIVE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 1 other fieldsHigh correlation
SEC_OVERDUE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 1 other fieldsHigh correlation
SEC_CURRENT_BALANCE is highly correlated with SEC_SANCTIONED_AMOUNT and 1 other fieldsHigh correlation
SEC_SANCTIONED_AMOUNT is highly correlated with SEC_CURRENT_BALANCE and 1 other fieldsHigh correlation
SEC_DISBURSED_AMOUNT is highly correlated with SEC_CURRENT_BALANCE and 1 other fieldsHigh correlation
NEW_ACCTS_IN_LAST_SIX_MONTHS is highly correlated with PRI_NO_OF_ACCTS and 1 other fieldsHigh correlation
DISBURSED_AMOUNT is highly correlated with ASSET_COSTHigh correlation
ASSET_COST is highly correlated with DISBURSED_AMOUNTHigh correlation
AADHAR_FLAG is highly correlated with VOTERID_FLAGHigh correlation
VOTERID_FLAG is highly correlated with AADHAR_FLAGHigh correlation
PERFORM_CNS_SCORE is highly correlated with PRI_NO_OF_ACCTS and 4 other fieldsHigh correlation
PRI_NO_OF_ACCTS is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_ACTIVE_ACCTS is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_CURRENT_BALANCE is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_SANCTIONED_AMOUNT is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
PRI_DISBURSED_AMOUNT is highly correlated with PERFORM_CNS_SCORE and 6 other fieldsHigh correlation
SEC_NO_OF_ACCTS is highly correlated with SEC_ACTIVE_ACCTS and 5 other fieldsHigh correlation
SEC_ACTIVE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
SEC_OVERDUE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 4 other fieldsHigh correlation
SEC_CURRENT_BALANCE is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
SEC_SANCTIONED_AMOUNT is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
SEC_DISBURSED_AMOUNT is highly correlated with SEC_NO_OF_ACCTS and 5 other fieldsHigh correlation
PRIMARY_INSTAL_AMT is highly correlated with PRI_NO_OF_ACCTS and 4 other fieldsHigh correlation
SEC_INSTAL_AMT is highly correlated with SEC_NO_OF_ACCTS and 4 other fieldsHigh correlation
NEW_ACCTS_IN_LAST_SIX_MONTHS is highly correlated with PRI_NO_OF_ACCTS and 4 other fieldsHigh correlation
AADHAR_FLAG is highly correlated with MOBILENO_AVL_FLAG and 1 other fieldsHigh correlation
MOBILENO_AVL_FLAG is highly correlated with AADHAR_FLAG and 7 other fieldsHigh correlation
DRIVING_FLAG is highly correlated with MOBILENO_AVL_FLAGHigh correlation
VOTERID_FLAG is highly correlated with AADHAR_FLAG and 1 other fieldsHigh correlation
EMPLOYMENT_TYPE is highly correlated with MOBILENO_AVL_FLAGHigh correlation
LOAN_DEFAULT is highly correlated with MOBILENO_AVL_FLAGHigh correlation
PERFORM_CNS_SCORE_DESCRIPTION is highly correlated with MOBILENO_AVL_FLAGHigh correlation
PAN_FLAG is highly correlated with MOBILENO_AVL_FLAGHigh correlation
PASSPORT_FLAG is highly correlated with MOBILENO_AVL_FLAGHigh correlation
UNIQUEID is highly correlated with DISBURSAL_DATEHigh correlation
DISBURSED_AMOUNT is highly correlated with ASSET_COST and 2 other fieldsHigh correlation
ASSET_COST is highly correlated with DISBURSED_AMOUNT and 1 other fieldsHigh correlation
LTV is highly correlated with DISBURSED_AMOUNTHigh correlation
BRANCH_ID is highly correlated with SUPPLIER_ID and 2 other fieldsHigh correlation
SUPPLIER_ID is highly correlated with BRANCH_IDHigh correlation
MANUFACTURER_ID is highly correlated with DISBURSED_AMOUNT and 1 other fieldsHigh correlation
CURRENT_PINCODE_ID is highly correlated with BRANCH_ID and 2 other fieldsHigh correlation
EMPLOYMENT_TYPE is highly correlated with LOAN_DEFAULTHigh correlation
DISBURSAL_DATE is highly correlated with UNIQUEIDHigh correlation
STATE_ID is highly correlated with BRANCH_ID and 3 other fieldsHigh correlation
AADHAR_FLAG is highly correlated with CURRENT_PINCODE_ID and 2 other fieldsHigh correlation
VOTERID_FLAG is highly correlated with STATE_ID and 1 other fieldsHigh correlation
PERFORM_CNS_SCORE is highly correlated with PERFORM_CNS_SCORE_DESCRIPTION and 1 other fieldsHigh correlation
PERFORM_CNS_SCORE_DESCRIPTION is highly correlated with PERFORM_CNS_SCOREHigh correlation
PRI_NO_OF_ACCTS is highly correlated with PRI_ACTIVE_ACCTS and 2 other fieldsHigh correlation
PRI_ACTIVE_ACCTS is highly correlated with PRI_NO_OF_ACCTS and 4 other fieldsHigh correlation
PRI_OVERDUE_ACCTS is highly correlated with PRI_CURRENT_BALANCE and 1 other fieldsHigh correlation
PRI_CURRENT_BALANCE is highly correlated with PRI_ACTIVE_ACCTS and 3 other fieldsHigh correlation
PRI_SANCTIONED_AMOUNT is highly correlated with PRI_ACTIVE_ACCTS and 2 other fieldsHigh correlation
PRI_DISBURSED_AMOUNT is highly correlated with PRI_ACTIVE_ACCTS and 2 other fieldsHigh correlation
SEC_NO_OF_ACCTS is highly correlated with SEC_ACTIVE_ACCTS and 2 other fieldsHigh correlation
SEC_ACTIVE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 2 other fieldsHigh correlation
SEC_OVERDUE_ACCTS is highly correlated with SEC_NO_OF_ACCTS and 2 other fieldsHigh correlation
SEC_CURRENT_BALANCE is highly correlated with SEC_SANCTIONED_AMOUNT and 2 other fieldsHigh correlation
SEC_SANCTIONED_AMOUNT is highly correlated with SEC_CURRENT_BALANCE and 1 other fieldsHigh correlation
SEC_DISBURSED_AMOUNT is highly correlated with SEC_CURRENT_BALANCE and 1 other fieldsHigh correlation
PRIMARY_INSTAL_AMT is highly correlated with PRI_NO_OF_ACCTSHigh correlation
SEC_INSTAL_AMT is highly correlated with SEC_CURRENT_BALANCE and 1 other fieldsHigh correlation
NEW_ACCTS_IN_LAST_SIX_MONTHS is highly correlated with PRI_NO_OF_ACCTS and 1 other fieldsHigh correlation
DELINQUENT_ACCTS_IN_LAST_SIX_MONTHS is highly correlated with PRI_OVERDUE_ACCTS and 4 other fieldsHigh correlation
LOAN_DEFAULT is highly correlated with EMPLOYMENT_TYPE and 1 other fieldsHigh correlation
EMPLOYMENT_TYPE has 281 (2.8%) missing values Missing
SEC_NO_OF_ACCTS is highly skewed (γ1 = 28.48033388) Skewed
SEC_ACTIVE_ACCTS is highly skewed (γ1 = 22.40878643) Skewed
SEC_OVERDUE_ACCTS is highly skewed (γ1 = 23.86177536) Skewed
SEC_CURRENT_BALANCE is highly skewed (γ1 = 46.91407737) Skewed
SEC_SANCTIONED_AMOUNT is highly skewed (γ1 = 42.77652496) Skewed
SEC_DISBURSED_AMOUNT is highly skewed (γ1 = 42.90236866) Skewed
PRIMARY_INSTAL_AMT is highly skewed (γ1 = 29.58056146) Skewed
SEC_INSTAL_AMT is highly skewed (γ1 = 34.50502137) Skewed
df_index has unique values Unique
UNIQUEID has unique values Unique
PERFORM_CNS_SCORE has 4954 (49.5%) zeros Zeros
PRI_NO_OF_ACCTS has 4954 (49.5%) zeros Zeros
PRI_ACTIVE_ACCTS has 5804 (58.0%) zeros Zeros
PRI_OVERDUE_ACCTS has 8832 (88.3%) zeros Zeros
PRI_CURRENT_BALANCE has 6016 (60.2%) zeros Zeros
PRI_SANCTIONED_AMOUNT has 5859 (58.6%) zeros Zeros
PRI_DISBURSED_AMOUNT has 5863 (58.6%) zeros Zeros
SEC_NO_OF_ACCTS has 9738 (97.4%) zeros Zeros
SEC_ACTIVE_ACCTS has 9822 (98.2%) zeros Zeros
SEC_OVERDUE_ACCTS has 9927 (99.3%) zeros Zeros
SEC_CURRENT_BALANCE has 9850 (98.5%) zeros Zeros
SEC_SANCTIONED_AMOUNT has 9824 (98.2%) zeros Zeros
SEC_DISBURSED_AMOUNT has 9826 (98.3%) zeros Zeros
PRIMARY_INSTAL_AMT has 6776 (67.8%) zeros Zeros
SEC_INSTAL_AMT has 9895 (99.0%) zeros Zeros
NEW_ACCTS_IN_LAST_SIX_MONTHS has 7737 (77.4%) zeros Zeros
DELINQUENT_ACCTS_IN_LAST_SIX_MONTHS has 9182 (91.8%) zeros Zeros
NO_OF_INQUIRIES has 8624 (86.2%) zeros Zeros

Reproduction

Analysis started2022-05-15 09:01:23.542216
Analysis finished2022-05-15 09:02:29.185157
Duration1 minute and 5.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66189.8121
Minimum0
Maximum133151
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:29.229076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6762.65
Q133606.75
median65566.5
Q399043.5
95-th percentile126227.15
Maximum133151
Range133151
Interquartile range (IQR)65436.75

Descriptive statistics

Standard deviation38166.35733
Coefficient of variation (CV)0.5766198168
Kurtosis-1.181553658
Mean66189.8121
Median Absolute Deviation (MAD)32743.5
Skewness0.0202609615
Sum661898121
Variance1456670832
MonotonicityNot monotonic
2022-05-15T12:02:29.309094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276281
 
< 0.1%
440961
 
< 0.1%
45371
 
< 0.1%
181851
 
< 0.1%
587831
 
< 0.1%
712771
 
< 0.1%
731861
 
< 0.1%
101711
 
< 0.1%
591811
 
< 0.1%
899231
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
01
< 0.1%
231
< 0.1%
331
< 0.1%
351
< 0.1%
701
< 0.1%
871
< 0.1%
891
< 0.1%
1101
< 0.1%
1151
< 0.1%
1191
< 0.1%
ValueCountFrequency (%)
1331511
< 0.1%
1331481
< 0.1%
1331411
< 0.1%
1331371
< 0.1%
1331201
< 0.1%
1331121
< 0.1%
1330981
< 0.1%
1330691
< 0.1%
1330471
< 0.1%
1330241
< 0.1%

UNIQUEID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean534957.3848
Minimum417465
Maximum658669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:29.387616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum417465
5-th percentile427691.5
Q1475017.75
median535230.5
Q3595294.5
95-th percentile642466.35
Maximum658669
Range241204
Interquartile range (IQR)120276.75

Descriptive statistics

Standard deviation68956.5145
Coefficient of variation (CV)0.1289009489
Kurtosis-1.215227603
Mean534957.3848
Median Absolute Deviation (MAD)60098.5
Skewness0.005476677063
Sum5349573848
Variance4755000893
MonotonicityNot monotonic
2022-05-15T12:02:29.464641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5736371
 
< 0.1%
6030471
 
< 0.1%
5752471
 
< 0.1%
5155041
 
< 0.1%
5760581
 
< 0.1%
5060231
 
< 0.1%
5891701
 
< 0.1%
5442961
 
< 0.1%
4212541
 
< 0.1%
4270941
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
4174651
< 0.1%
4175291
< 0.1%
4175831
< 0.1%
4175861
< 0.1%
4175871
< 0.1%
4176371
< 0.1%
4176501
< 0.1%
4176691
< 0.1%
4176741
< 0.1%
4176841
< 0.1%
ValueCountFrequency (%)
6586691
< 0.1%
6586581
< 0.1%
6586531
< 0.1%
6540661
< 0.1%
6540091
< 0.1%
6539621
< 0.1%
6539541
< 0.1%
6539501
< 0.1%
6539431
< 0.1%
6539341
< 0.1%

DISBURSED_AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3447
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54271.6716
Minimum13664
Maximum153318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:29.541659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13664
5-th percentile35362.1
Q147153.5
median53728.5
Q360263
95-th percentile73717
Maximum153318
Range139654
Interquartile range (IQR)13109.5

Descriptive statistics

Standard deviation12304.73889
Coefficient of variation (CV)0.2267248922
Kurtosis3.205153982
Mean54271.6716
Median Absolute Deviation (MAD)6564.5
Skewness0.7942214938
Sum542716716
Variance151406599.2
MonotonicityNot monotonic
2022-05-15T12:02:29.617676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48349105
 
1.1%
5525991
 
0.9%
5230389
 
0.9%
4734987
 
0.9%
5130384
 
0.8%
5330375
 
0.8%
4634973
 
0.7%
5725972
 
0.7%
5030372
 
0.7%
4534962
 
0.6%
Other values (3437)9190
91.9%
ValueCountFrequency (%)
136642
< 0.1%
141401
< 0.1%
149301
< 0.1%
156191
< 0.1%
159101
< 0.1%
165001
< 0.1%
166191
< 0.1%
172391
< 0.1%
176341
< 0.1%
177391
< 0.1%
ValueCountFrequency (%)
1533181
< 0.1%
1405231
< 0.1%
1325311
< 0.1%
1304991
< 0.1%
1295981
< 0.1%
1271031
< 0.1%
1232081
< 0.1%
1212381
< 0.1%
1186181
< 0.1%
1183281
< 0.1%

ASSET_COST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7347
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75604.0799
Minimum38055
Maximum247078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:29.700695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum38055
5-th percentile58411.4
Q165581.75
median70828
Q378987.25
95-th percentile109300.2
Maximum247078
Range209023
Interquartile range (IQR)13405.5

Descriptive statistics

Standard deviation17978.81777
Coefficient of variation (CV)0.2378022164
Kurtosis8.657291011
Mean75604.0799
Median Absolute Deviation (MAD)6172
Skewness2.379476488
Sum756040799
Variance323237888.3
MonotonicityNot monotonic
2022-05-15T12:02:29.778712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200032
 
0.3%
6800032
 
0.3%
7500028
 
0.3%
6700027
 
0.3%
7700023
 
0.2%
6900021
 
0.2%
6600020
 
0.2%
6300020
 
0.2%
7000019
 
0.2%
7300019
 
0.2%
Other values (7337)9759
97.6%
ValueCountFrequency (%)
380551
< 0.1%
387521
< 0.1%
392171
< 0.1%
395361
< 0.1%
401671
< 0.1%
401752
< 0.1%
406001
< 0.1%
407001
< 0.1%
407681
< 0.1%
408931
< 0.1%
ValueCountFrequency (%)
2470781
< 0.1%
2065181
< 0.1%
1993711
< 0.1%
1964511
< 0.1%
1909601
< 0.1%
1891301
< 0.1%
1881341
< 0.1%
1855311
< 0.1%
1806611
< 0.1%
1800001
< 0.1%

LTV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3461
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.867973
Minimum16.6
Maximum94.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:29.857730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16.6
5-th percentile52.998
Q169.125
median76.87
Q383.52
95-th percentile89.34
Maximum94.99
Range78.39
Interquartile range (IQR)14.395

Descriptive statistics

Standard deviation11.24117739
Coefficient of variation (CV)0.1501466774
Kurtosis1.444502947
Mean74.867973
Median Absolute Deviation (MAD)7.11
Skewness-1.089845353
Sum748679.73
Variance126.364069
MonotonicityNot monotonic
2022-05-15T12:02:29.934748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85208
 
2.1%
84.9951
 
0.5%
79.9928
 
0.3%
79.923
 
0.2%
84.9519
 
0.2%
74.9318
 
0.2%
79.9218
 
0.2%
74.9917
 
0.2%
8017
 
0.2%
84.9617
 
0.2%
Other values (3451)9584
95.8%
ValueCountFrequency (%)
16.61
< 0.1%
18.081
< 0.1%
21.81
< 0.1%
22.661
< 0.1%
22.761
< 0.1%
22.791
< 0.1%
23.721
< 0.1%
23.761
< 0.1%
24.21
< 0.1%
26.881
< 0.1%
ValueCountFrequency (%)
94.991
 
< 0.1%
94.951
 
< 0.1%
94.923
< 0.1%
94.911
 
< 0.1%
94.882
< 0.1%
94.851
 
< 0.1%
94.821
 
< 0.1%
94.811
 
< 0.1%
94.82
< 0.1%
94.781
 
< 0.1%

BRANCH_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.8098
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:30.016767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median61
Q3121
95-th percentile249.05
Maximum261
Range260
Interquartile range (IQR)107

Descriptive statistics

Standard deviation70.32340693
Coefficient of variation (CV)0.965850846
Kurtosis0.3227596022
Mean72.8098
Median Absolute Deviation (MAD)50
Skewness1.049010129
Sum728098
Variance4945.381562
MonotonicityNot monotonic
2022-05-15T12:02:30.195813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2546
 
5.5%
67446
 
4.5%
3413
 
4.1%
5398
 
4.0%
36382
 
3.8%
34375
 
3.8%
136334
 
3.3%
16276
 
2.8%
19246
 
2.5%
18229
 
2.3%
Other values (72)6355
63.5%
ValueCountFrequency (%)
1228
2.3%
2546
5.5%
3413
4.1%
5398
4.0%
7138
 
1.4%
8145
 
1.5%
9102
 
1.0%
10192
 
1.9%
11198
 
2.0%
13137
 
1.4%
ValueCountFrequency (%)
2619
 
0.1%
26016
 
0.2%
25917
 
0.2%
25818
 
0.2%
25746
 
0.5%
25562
 
0.6%
25470
 
0.7%
251184
1.8%
25078
0.8%
24947
 
0.5%

SUPPLIER_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1991
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19677.8876
Minimum12312
Maximum24793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:30.275827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12312
5-th percentile14180
Q116574.75
median20335
Q323029.75
95-th percentile24130
Maximum24793
Range12481
Interquartile range (IQR)6455

Descriptive statistics

Standard deviation3497.881366
Coefficient of variation (CV)0.1777569543
Kurtosis-1.476533659
Mean19677.8876
Median Absolute Deviation (MAD)3090
Skewness-0.1766516394
Sum196778876
Variance12235174.05
MonotonicityNot monotonic
2022-05-15T12:02:30.357846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1831765
 
0.7%
2198058
 
0.6%
1798051
 
0.5%
1853250
 
0.5%
1569450
 
0.5%
1566350
 
0.5%
1423450
 
0.5%
1414548
 
0.5%
2272746
 
0.5%
2112445
 
0.4%
Other values (1981)9487
94.9%
ValueCountFrequency (%)
123122
 
< 0.1%
123746
0.1%
124415
0.1%
124564
< 0.1%
125002
 
< 0.1%
127973
< 0.1%
128421
 
< 0.1%
128781
 
< 0.1%
131312
 
< 0.1%
132951
 
< 0.1%
ValueCountFrequency (%)
247931
 
< 0.1%
247771
 
< 0.1%
247703
< 0.1%
247611
 
< 0.1%
247541
 
< 0.1%
247452
< 0.1%
247441
 
< 0.1%
247282
< 0.1%
247271
 
< 0.1%
247211
 
< 0.1%

MANUFACTURER_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.2577
Minimum45
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:30.425861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile45
Q148
median86
Q386
95-th percentile86
Maximum145
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.35396297
Coefficient of variation (CV)0.3227650207
Kurtosis-0.6984929472
Mean69.2577
Median Absolute Deviation (MAD)34
Skewness0.3982010136
Sum692577
Variance499.6996607
MonotonicityNot monotonic
2022-05-15T12:02:30.477873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
864694
46.9%
452423
24.2%
511128
 
11.3%
48735
 
7.3%
120448
 
4.5%
49440
 
4.4%
6795
 
0.9%
14537
 
0.4%
ValueCountFrequency (%)
452423
24.2%
48735
 
7.3%
49440
 
4.4%
511128
 
11.3%
6795
 
0.9%
864694
46.9%
120448
 
4.5%
14537
 
0.4%
ValueCountFrequency (%)
14537
 
0.4%
120448
 
4.5%
864694
46.9%
6795
 
0.9%
511128
 
11.3%
49440
 
4.4%
48735
 
7.3%
452423
24.2%

CURRENT_PINCODE_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3141
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3402.1104
Minimum2
Maximum7333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:30.544964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile231
Q11514
median2964
Q35681
95-th percentile6940.05
Maximum7333
Range7331
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation2240.517613
Coefficient of variation (CV)0.658566992
Kurtosis-1.286981438
Mean3402.1104
Median Absolute Deviation (MAD)1915
Skewness0.2704608709
Sum34021104
Variance5019919.173
MonotonicityNot monotonic
2022-05-15T12:02:30.622977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
257891
 
0.9%
144680
 
0.8%
298955
 
0.5%
294343
 
0.4%
151538
 
0.4%
237837
 
0.4%
279035
 
0.4%
179435
 
0.4%
150934
 
0.3%
278234
 
0.3%
Other values (3131)9518
95.2%
ValueCountFrequency (%)
23
 
< 0.1%
31
 
< 0.1%
45
 
0.1%
513
0.1%
65
 
0.1%
77
0.1%
91
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
73332
< 0.1%
73312
< 0.1%
73281
< 0.1%
73241
< 0.1%
73211
< 0.1%
73152
< 0.1%
73111
< 0.1%
73081
< 0.1%
73041
< 0.1%
73022
< 0.1%
Distinct5213
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum1949-09-15 00:00:00
Maximum2000-09-24 00:00:00
2022-05-15T12:02:30.707011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:30.789044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EMPLOYMENT_TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing281
Missing (%)2.8%
Memory size78.2 KiB
Self employed
5512 
Salaried
4207 

Length

Max length13
Median length13
Mean length10.83568268
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowSelf employed
3rd rowSalaried
4th rowSelf employed
5th rowSelf employed

Common Values

ValueCountFrequency (%)
Self employed5512
55.1%
Salaried4207
42.1%
(Missing)281
 
2.8%

Length

2022-05-15T12:02:30.869075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:30.908090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
self5512
36.2%
employed5512
36.2%
salaried4207
27.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DISBURSAL_DATE
Date

HIGH CORRELATION

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2018-08-01 00:00:00
Maximum2018-10-31 00:00:00
2022-05-15T12:02:30.956110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:31.037142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

STATE_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2421
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:31.119174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q310
95-th percentile16
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.463146822
Coefficient of variation (CV)0.6162779888
Kurtosis-0.3300524869
Mean7.2421
Median Absolute Deviation (MAD)3
Skewness0.8171253758
Sum72421
Variance19.91967956
MonotonicityNot monotonic
2022-05-15T12:02:31.179199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
41960
19.6%
31449
14.5%
61404
14.0%
13776
 
7.8%
9680
 
6.8%
8628
 
6.3%
5440
 
4.4%
14400
 
4.0%
1389
 
3.9%
11300
 
3.0%
Other values (12)1574
15.7%
ValueCountFrequency (%)
1389
 
3.9%
2179
 
1.8%
31449
14.5%
41960
19.6%
5440
 
4.4%
61404
14.0%
7281
 
2.8%
8628
 
6.3%
9680
 
6.8%
10145
 
1.5%
ValueCountFrequency (%)
222
 
< 0.1%
219
 
0.1%
2010
 
0.1%
1945
 
0.4%
18210
 
2.1%
17159
 
1.6%
16127
 
1.3%
15209
 
2.1%
14400
4.0%
13776
7.8%

EMPLOYEE_CODE_ID
Real number (ℝ≥0)

Distinct2645
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1569.318
Minimum1
Maximum3775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:31.251226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile157.9
Q1740
median1478
Q32393
95-th percentile3188.05
Maximum3775
Range3774
Interquartile range (IQR)1653

Descriptive statistics

Standard deviation972.7128304
Coefficient of variation (CV)0.6198315641
Kurtosis-1.07329135
Mean1569.318
Median Absolute Deviation (MAD)807
Skewness0.2150110121
Sum15693180
Variance946170.2505
MonotonicityNot monotonic
2022-05-15T12:02:31.327783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25531
 
0.3%
254631
 
0.3%
215322
 
0.2%
84521
 
0.2%
62021
 
0.2%
260119
 
0.2%
62818
 
0.2%
183118
 
0.2%
143917
 
0.2%
194617
 
0.2%
Other values (2635)9785
97.9%
ValueCountFrequency (%)
14
< 0.1%
38
0.1%
42
 
< 0.1%
54
< 0.1%
74
< 0.1%
92
 
< 0.1%
103
 
< 0.1%
116
0.1%
124
< 0.1%
152
 
< 0.1%
ValueCountFrequency (%)
37751
< 0.1%
37651
< 0.1%
37601
< 0.1%
37591
< 0.1%
37531
< 0.1%
37401
< 0.1%
37252
< 0.1%
37231
< 0.1%
37211
< 0.1%
37201
< 0.1%

MOBILENO_AVL_FLAG
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110000
100.0%

Length

2022-05-15T12:02:31.494820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:31.532488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
110000
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AADHAR_FLAG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
8402 
0
1598 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
18402
84.0%
01598
 
16.0%

Length

2022-05-15T12:02:31.566496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:31.604504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
18402
84.0%
01598
 
16.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAN_FLAG
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9255 
1
 
745

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09255
92.5%
1745
 
7.4%

Length

2022-05-15T12:02:31.641513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:31.678521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09255
92.5%
1745
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VOTERID_FLAG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8552 
1
1448 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
08552
85.5%
11448
 
14.5%

Length

2022-05-15T12:02:31.715529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:31.752538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
08552
85.5%
11448
 
14.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DRIVING_FLAG
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9777 
1
 
223

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09777
97.8%
1223
 
2.2%

Length

2022-05-15T12:02:31.790546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:31.828555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09777
97.8%
1223
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PASSPORT_FLAG
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9975 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09975
99.8%
125
 
0.2%

Length

2022-05-15T12:02:31.864563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:31.901572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
09975
99.8%
125
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PERFORM_CNS_SCORE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct464
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.6837
Minimum0
Maximum879
Zeros4954
Zeros (%)49.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:31.949583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q3679
95-th percentile825
Maximum879
Range879
Interquartile range (IQR)679

Descriptive statistics

Standard deviation338.6778902
Coefficient of variation (CV)1.15320629
Kurtosis-1.656769508
Mean293.6837
Median Absolute Deviation (MAD)15
Skewness0.4183197866
Sum2936837
Variance114702.7133
MonotonicityNot monotonic
2022-05-15T12:02:32.023599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04954
49.5%
738364
 
3.6%
300362
 
3.6%
825320
 
3.2%
15170
 
1.7%
763155
 
1.6%
17154
 
1.5%
16126
 
1.3%
708102
 
1.0%
73795
 
0.9%
Other values (454)3198
32.0%
ValueCountFrequency (%)
04954
49.5%
1430
 
0.3%
15170
 
1.7%
16126
 
1.3%
17154
 
1.5%
1865
 
0.7%
300362
 
3.6%
3021
 
< 0.1%
3056
 
0.1%
3062
 
< 0.1%
ValueCountFrequency (%)
8792
 
< 0.1%
8731
 
< 0.1%
8701
 
< 0.1%
8581
 
< 0.1%
8534
 
< 0.1%
84523
0.2%
8443
 
< 0.1%
8421
 
< 0.1%
8411
 
< 0.1%
8396
 
0.1%

PERFORM_CNS_SCORE_DESCRIPTION
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
No Bureau History Available
4954 
C-Very Low Risk
682 
A-Very Low Risk
602 
D-Very Low Risk
 
483
B-Very Low Risk
 
424
Other values (14)
2855 

Length

Max length55
Median length27
Mean length22.1101
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Bureau History Available
2nd rowNo Bureau History Available
3rd rowB-Very Low Risk
4th rowNo Bureau History Available
5th rowNo Bureau History Available

Common Values

ValueCountFrequency (%)
No Bureau History Available4954
49.5%
C-Very Low Risk682
 
6.8%
A-Very Low Risk602
 
6.0%
D-Very Low Risk483
 
4.8%
B-Very Low Risk424
 
4.2%
K-High Risk402
 
4.0%
F-Low Risk374
 
3.7%
M-Very High Risk362
 
3.6%
H-Medium Risk317
 
3.2%
I-Medium Risk256
 
2.6%
Other values (9)1144
 
11.4%

Length

2022-05-15T12:02:32.093616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
available5343
15.0%
no5145
14.4%
history5124
14.4%
bureau4954
13.9%
risk4501
12.6%
low2191
 
6.1%
not869
 
2.4%
c-very682
 
1.9%
a-very602
 
1.7%
scored545
 
1.5%
Other values (29)5737
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PRI_NO_OF_ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4961
Minimum0
Maximum99
Zeros4954
Zeros (%)49.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:32.161640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.088454974
Coefficient of variation (CV)2.038562147
Kurtosis42.18317646
Mean2.4961
Median Absolute Deviation (MAD)1
Skewness4.901919475
Sum24961
Variance25.89237403
MonotonicityNot monotonic
2022-05-15T12:02:32.240660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04954
49.5%
11512
 
15.1%
2865
 
8.6%
3529
 
5.3%
4405
 
4.0%
5319
 
3.2%
6257
 
2.6%
7207
 
2.1%
8151
 
1.5%
9131
 
1.3%
Other values (44)670
 
6.7%
ValueCountFrequency (%)
04954
49.5%
11512
 
15.1%
2865
 
8.6%
3529
 
5.3%
4405
 
4.0%
5319
 
3.2%
6257
 
2.6%
7207
 
2.1%
8151
 
1.5%
9131
 
1.3%
ValueCountFrequency (%)
991
 
< 0.1%
761
 
< 0.1%
681
 
< 0.1%
671
 
< 0.1%
651
 
< 0.1%
572
< 0.1%
551
 
< 0.1%
521
 
< 0.1%
491
 
< 0.1%
463
< 0.1%

PRI_ACTIVE_ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0669
Minimum0
Maximum34
Zeros5804
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:32.311676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum34
Range34
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.013263448
Coefficient of variation (CV)1.887021697
Kurtosis28.95482826
Mean1.0669
Median Absolute Deviation (MAD)0
Skewness4.018695476
Sum10669
Variance4.053229713
MonotonicityNot monotonic
2022-05-15T12:02:32.374692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
05804
58.0%
11889
 
18.9%
2911
 
9.1%
3507
 
5.1%
4308
 
3.1%
5215
 
2.1%
6114
 
1.1%
785
 
0.9%
844
 
0.4%
933
 
0.3%
Other values (15)90
 
0.9%
ValueCountFrequency (%)
05804
58.0%
11889
 
18.9%
2911
 
9.1%
3507
 
5.1%
4308
 
3.1%
5215
 
2.1%
6114
 
1.1%
785
 
0.9%
844
 
0.4%
933
 
0.3%
ValueCountFrequency (%)
341
 
< 0.1%
282
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
202
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
172
 
< 0.1%
167
0.1%
154
< 0.1%

PRI_OVERDUE_ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1626
Minimum0
Maximum23
Zeros8832
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:32.436706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5963193772
Coefficient of variation (CV)3.667400844
Kurtosis312.1682989
Mean0.1626
Median Absolute Deviation (MAD)0
Skewness11.70861455
Sum1626
Variance0.3555967997
MonotonicityNot monotonic
2022-05-15T12:02:32.488718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
08832
88.3%
1892
 
8.9%
2193
 
1.9%
348
 
0.5%
420
 
0.2%
65
 
0.1%
54
 
< 0.1%
72
 
< 0.1%
171
 
< 0.1%
81
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
08832
88.3%
1892
 
8.9%
2193
 
1.9%
348
 
0.5%
420
 
0.2%
54
 
< 0.1%
65
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
231
 
< 0.1%
171
 
< 0.1%
121
 
< 0.1%
81
 
< 0.1%
72
 
< 0.1%
65
 
0.1%
54
 
< 0.1%
420
 
0.2%
348
 
0.5%
2193
1.9%

PRI_CURRENT_BALANCE
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3827
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180406.2596
Minimum-33286
Maximum36939084
Zeros6016
Zeros (%)60.2%
Negative18
Negative (%)0.2%
Memory size78.2 KiB
2022-05-15T12:02:32.555734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-33286
5-th percentile0
Q10
median0
Q335437.25
95-th percentile862995.75
Maximum36939084
Range36972370
Interquartile range (IQR)35437.25

Descriptive statistics

Standard deviation1070291.312
Coefficient of variation (CV)5.932672814
Kurtosis527.2918274
Mean180406.2596
Median Absolute Deviation (MAD)0
Skewness19.90717877
Sum1804062596
Variance1.145523492 × 1012
MonotonicityNot monotonic
2022-05-15T12:02:32.633751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06016
60.2%
50007
 
0.1%
8006
 
0.1%
300005
 
0.1%
220004
 
< 0.1%
200004
 
< 0.1%
590004
 
< 0.1%
16004
 
< 0.1%
400003
 
< 0.1%
250003
 
< 0.1%
Other values (3817)3944
39.4%
ValueCountFrequency (%)
-332861
< 0.1%
-155221
< 0.1%
-71141
< 0.1%
-67111
< 0.1%
-29891
< 0.1%
-8261
< 0.1%
-3111
< 0.1%
-2141
< 0.1%
-2011
< 0.1%
-521
< 0.1%
ValueCountFrequency (%)
369390841
< 0.1%
339450921
< 0.1%
320274201
< 0.1%
289909201
< 0.1%
281992561
< 0.1%
271288941
< 0.1%
247051121
< 0.1%
215755901
< 0.1%
182441961
< 0.1%
172164161
< 0.1%

PRI_SANCTIONED_AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2805
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231450.0578
Minimum0
Maximum42868148
Zeros5859
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:32.810791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q363834.25
95-th percentile1100121.3
Maximum42868148
Range42868148
Interquartile range (IQR)63834.25

Descriptive statistics

Standard deviation1286267.413
Coefficient of variation (CV)5.557429646
Kurtosis479.8400705
Mean231450.0578
Median Absolute Deviation (MAD)0
Skewness18.98218808
Sum2314500578
Variance1.654483857 × 1012
MonotonicityNot monotonic
2022-05-15T12:02:32.886810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05859
58.6%
5000067
 
0.7%
3000064
 
0.6%
10000050
 
0.5%
2000046
 
0.5%
4000042
 
0.4%
2500039
 
0.4%
20000025
 
0.2%
6000023
 
0.2%
30000023
 
0.2%
Other values (2795)3762
37.6%
ValueCountFrequency (%)
05859
58.6%
22
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
63
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
121
 
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
428681481
< 0.1%
388514161
< 0.1%
380924041
< 0.1%
376304241
< 0.1%
293540521
< 0.1%
288934961
< 0.1%
272197721
< 0.1%
255103161
< 0.1%
251189001
< 0.1%
233060641
< 0.1%

PRI_DISBURSED_AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2924
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230204.9529
Minimum0
Maximum42868148
Zeros5863
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:32.964828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q362033.25
95-th percentile1095250
Maximum42868148
Range42868148
Interquartile range (IQR)62033.25

Descriptive statistics

Standard deviation1284395.948
Coefficient of variation (CV)5.579358446
Kurtosis481.0344425
Mean230204.9529
Median Absolute Deviation (MAD)0
Skewness19.01754561
Sum2302049529
Variance1.649672952 × 1012
MonotonicityNot monotonic
2022-05-15T12:02:33.039845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05863
58.6%
5000066
 
0.7%
3000059
 
0.6%
10000048
 
0.5%
4000039
 
0.4%
2000034
 
0.3%
2500029
 
0.3%
30000024
 
0.2%
20000024
 
0.2%
4500022
 
0.2%
Other values (2914)3792
37.9%
ValueCountFrequency (%)
05863
58.6%
22
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
63
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
121
 
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
428681481
< 0.1%
386485681
< 0.1%
381022041
< 0.1%
376298481
< 0.1%
293540521
< 0.1%
288934961
< 0.1%
272197721
< 0.1%
255103161
< 0.1%
251189001
< 0.1%
233060641
< 0.1%

SEC_NO_OF_ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0664
Minimum0
Maximum38
Zeros9738
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.105860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7341968023
Coefficient of variation (CV)11.05718076
Kurtosis1154.133825
Mean0.0664
Median Absolute Deviation (MAD)0
Skewness28.48033388
Sum664
Variance0.5390449445
MonotonicityNot monotonic
2022-05-15T12:02:33.159874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
09738
97.4%
1143
 
1.4%
257
 
0.6%
320
 
0.2%
417
 
0.2%
86
 
0.1%
64
 
< 0.1%
113
 
< 0.1%
52
 
< 0.1%
92
 
< 0.1%
Other values (7)8
 
0.1%
ValueCountFrequency (%)
09738
97.4%
1143
 
1.4%
257
 
0.6%
320
 
0.2%
417
 
0.2%
52
 
< 0.1%
64
 
< 0.1%
71
 
< 0.1%
86
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
381
 
< 0.1%
311
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
131
 
< 0.1%
113
< 0.1%
102
 
< 0.1%
92
 
< 0.1%
86
0.1%
71
 
< 0.1%

SEC_ACTIVE_ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0306
Minimum0
Maximum14
Zeros9822
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.216896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3278328651
Coefficient of variation (CV)10.71349232
Kurtosis702.5196587
Mean0.0306
Median Absolute Deviation (MAD)0
Skewness22.40878643
Sum306
Variance0.1074743874
MonotonicityNot monotonic
2022-05-15T12:02:33.265915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09822
98.2%
1122
 
1.2%
233
 
0.3%
311
 
0.1%
45
 
0.1%
112
 
< 0.1%
82
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
09822
98.2%
1122
 
1.2%
233
 
0.3%
311
 
0.1%
45
 
0.1%
61
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
112
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
112
 
< 0.1%
82
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
45
 
0.1%
311
 
0.1%
233
 
0.3%
1122
 
1.2%
09822
98.2%

SEC_OVERDUE_ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0098
Minimum0
Maximum6
Zeros9927
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.319935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1417955239
Coefficient of variation (CV)14.46893101
Kurtosis759.2454639
Mean0.0098
Median Absolute Deviation (MAD)0
Skewness23.86177536
Sum98
Variance0.0201059706
MonotonicityNot monotonic
2022-05-15T12:02:33.368953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
09927
99.3%
162
 
0.6%
25
 
0.1%
52
 
< 0.1%
32
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
09927
99.3%
162
 
0.6%
25
 
0.1%
32
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
52
 
< 0.1%
41
 
< 0.1%
32
 
< 0.1%
25
 
0.1%
162
 
0.6%
09927
99.3%

SEC_CURRENT_BALANCE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7049.7148
Minimum0
Maximum10779261
Zeros9850
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.439981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10779261
Range10779261
Interquartile range (IQR)0

Descriptive statistics

Standard deviation194151.8588
Coefficient of variation (CV)27.54038487
Kurtosis2433.267893
Mean7049.7148
Median Absolute Deviation (MAD)0
Skewness46.91407737
Sum70497148
Variance3.769494428 × 1010
MonotonicityNot monotonic
2022-05-15T12:02:33.521011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09850
98.5%
272962
 
< 0.1%
2106461
 
< 0.1%
5411341
 
< 0.1%
1373531
 
< 0.1%
19000001
 
< 0.1%
540931
 
< 0.1%
11800161
 
< 0.1%
51621
 
< 0.1%
13766031
 
< 0.1%
Other values (140)140
 
1.4%
ValueCountFrequency (%)
09850
98.5%
991
 
< 0.1%
1001
 
< 0.1%
1831
 
< 0.1%
2791
 
< 0.1%
6081
 
< 0.1%
8001
 
< 0.1%
13581
 
< 0.1%
16871
 
< 0.1%
17221
 
< 0.1%
ValueCountFrequency (%)
107792611
< 0.1%
107160391
< 0.1%
93281571
< 0.1%
36187371
< 0.1%
35580711
< 0.1%
25247471
< 0.1%
20676011
< 0.1%
19000001
< 0.1%
16510161
< 0.1%
14639301
< 0.1%

SEC_SANCTIONED_AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9512.7601
Minimum0
Maximum11900000
Zeros9824
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.600041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11900000
Range11900000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation223043.16
Coefficient of variation (CV)23.44673445
Kurtosis2100.033743
Mean9512.7601
Median Absolute Deviation (MAD)0
Skewness42.77652496
Sum95127601
Variance4.97482512 × 1010
MonotonicityNot monotonic
2022-05-15T12:02:33.679938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09824
98.2%
500007
 
0.1%
400004
 
< 0.1%
430004
 
< 0.1%
290003
 
< 0.1%
570003
 
< 0.1%
350002
 
< 0.1%
2000002
 
< 0.1%
680002
 
< 0.1%
5000002
 
< 0.1%
Other values (140)147
 
1.5%
ValueCountFrequency (%)
09824
98.2%
821
 
< 0.1%
991
 
< 0.1%
2501
 
< 0.1%
3461
 
< 0.1%
7711
 
< 0.1%
13581
 
< 0.1%
30511
 
< 0.1%
57511
 
< 0.1%
63431
 
< 0.1%
ValueCountFrequency (%)
119000001
< 0.1%
113657901
< 0.1%
110000001
< 0.1%
39531331
< 0.1%
35030031
< 0.1%
31830001
< 0.1%
30981461
< 0.1%
27960001
< 0.1%
22000001
< 0.1%
21000001
< 0.1%

SEC_DISBURSED_AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct158
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9433.451
Minimum0
Maximum11900000
Zeros9826
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.760977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11900000
Range11900000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation222742.932
Coefficient of variation (CV)23.61203043
Kurtosis2110.600808
Mean9433.451
Median Absolute Deviation (MAD)0
Skewness42.90236866
Sum94334510
Variance4.961441377 × 1010
MonotonicityNot monotonic
2022-05-15T12:02:33.841005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09826
98.3%
500004
 
< 0.1%
430004
 
< 0.1%
400003
 
< 0.1%
290003
 
< 0.1%
480002
 
< 0.1%
170502
 
< 0.1%
5000002
 
< 0.1%
20000002
 
< 0.1%
2000002
 
< 0.1%
Other values (148)150
 
1.5%
ValueCountFrequency (%)
09826
98.3%
821
 
< 0.1%
991
 
< 0.1%
2501
 
< 0.1%
3461
 
< 0.1%
4791
 
< 0.1%
7711
 
< 0.1%
11001
 
< 0.1%
13581
 
< 0.1%
30511
 
< 0.1%
ValueCountFrequency (%)
119000001
< 0.1%
113657901
< 0.1%
110000001
< 0.1%
39531331
< 0.1%
35030031
< 0.1%
31153121
< 0.1%
29937391
< 0.1%
27960001
< 0.1%
22000001
< 0.1%
21000001
< 0.1%

PRIMARY_INSTAL_AMT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2789
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12425.418
Minimum0
Maximum5718114
Zeros6776
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:33.922032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32091.75
95-th percentile27521.15
Maximum5718114
Range5718114
Interquartile range (IQR)2091.75

Descriptive statistics

Standard deviation109509.4272
Coefficient of variation (CV)8.813339492
Kurtosis1242.620511
Mean12425.418
Median Absolute Deviation (MAD)0
Skewness29.58056146
Sum124254180
Variance1.199231464 × 1010
MonotonicityNot monotonic
2022-05-15T12:02:33.997060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06776
67.8%
162012
 
0.1%
20009
 
0.1%
13507
 
0.1%
17006
 
0.1%
13996
 
0.1%
12506
 
0.1%
16006
 
0.1%
19005
 
0.1%
10005
 
0.1%
Other values (2779)3162
31.6%
ValueCountFrequency (%)
06776
67.8%
11
 
< 0.1%
31
 
< 0.1%
71
 
< 0.1%
102
 
< 0.1%
131
 
< 0.1%
201
 
< 0.1%
212
 
< 0.1%
231
 
< 0.1%
251
 
< 0.1%
ValueCountFrequency (%)
57181141
< 0.1%
48978371
< 0.1%
26140001
< 0.1%
25526501
< 0.1%
20823921
< 0.1%
19135551
< 0.1%
17724111
< 0.1%
16417791
< 0.1%
15005101
< 0.1%
13960001
< 0.1%

SEC_INSTAL_AMT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct106
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.0842
Minimum0
Maximum280000
Zeros9895
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:34.170109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum280000
Range280000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5467.922584
Coefficient of variation (CV)23.76487644
Kurtosis1345.73446
Mean230.0842
Median Absolute Deviation (MAD)0
Skewness34.50502137
Sum2300842
Variance29898177.39
MonotonicityNot monotonic
2022-05-15T12:02:34.243125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09895
99.0%
14041
 
< 0.1%
10461
 
< 0.1%
29901
 
< 0.1%
235281
 
< 0.1%
146111
 
< 0.1%
22701
 
< 0.1%
83381
 
< 0.1%
2531
 
< 0.1%
12581
 
< 0.1%
Other values (96)96
 
1.0%
ValueCountFrequency (%)
09895
99.0%
661
 
< 0.1%
2311
 
< 0.1%
2531
 
< 0.1%
7841
 
< 0.1%
8331
 
< 0.1%
10251
 
< 0.1%
10461
 
< 0.1%
11001
 
< 0.1%
11661
 
< 0.1%
ValueCountFrequency (%)
2800001
< 0.1%
2192141
< 0.1%
1810001
< 0.1%
1628821
< 0.1%
1605691
< 0.1%
1469201
< 0.1%
1350001
< 0.1%
1140001
< 0.1%
1029991
< 0.1%
992351
< 0.1%

NEW_ACCTS_IN_LAST_SIX_MONTHS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3886
Minimum0
Maximum14
Zeros7737
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:34.308153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9457692572
Coefficient of variation (CV)2.433786045
Kurtosis25.20272341
Mean0.3886
Median Absolute Deviation (MAD)0
Skewness4.071617201
Sum3886
Variance0.8944794879
MonotonicityNot monotonic
2022-05-15T12:02:34.359228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
07737
77.4%
11414
 
14.1%
2477
 
4.8%
3185
 
1.8%
492
 
0.9%
541
 
0.4%
624
 
0.2%
714
 
0.1%
88
 
0.1%
93
 
< 0.1%
Other values (3)5
 
0.1%
ValueCountFrequency (%)
07737
77.4%
11414
 
14.1%
2477
 
4.8%
3185
 
1.8%
492
 
0.9%
541
 
0.4%
624
 
0.2%
714
 
0.1%
88
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
131
 
< 0.1%
103
 
< 0.1%
93
 
< 0.1%
88
 
0.1%
714
 
0.1%
624
 
0.2%
541
 
0.4%
492
0.9%
3185
1.8%

DELINQUENT_ACCTS_IN_LAST_SIX_MONTHS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1044
Minimum0
Maximum11
Zeros9182
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:34.417315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4165549085
Coefficient of variation (CV)3.989989545
Kurtosis105.5409615
Mean0.1044
Median Absolute Deviation (MAD)0
Skewness7.502574674
Sum1044
Variance0.1735179918
MonotonicityNot monotonic
2022-05-15T12:02:34.468409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09182
91.8%
1662
 
6.6%
2126
 
1.3%
316
 
0.2%
45
 
0.1%
73
 
< 0.1%
62
 
< 0.1%
52
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
09182
91.8%
1662
 
6.6%
2126
 
1.3%
316
 
0.2%
45
 
0.1%
52
 
< 0.1%
62
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
62
 
< 0.1%
52
 
< 0.1%
45
 
0.1%
316
 
0.2%
2126
 
1.3%
1662
 
6.6%
09182
91.8%

AVERAGE_ACCT_AGE
Categorical

HIGH CARDINALITY

Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0yrs 0mon
5084 
0yrs 6mon
 
258
0yrs 10mon
 
235
0yrs 7mon
 
233
1yrs 0mon
 
224
Other values (107)
3966 

Length

Max length10
Median length9
Mean length9.0769
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.2%

Sample

1st row0yrs 0mon
2nd row0yrs 0mon
3rd row0yrs 5mon
4th row0yrs 0mon
5th row0yrs 0mon

Common Values

ValueCountFrequency (%)
0yrs 0mon5084
50.8%
0yrs 6mon258
 
2.6%
0yrs 10mon235
 
2.4%
0yrs 7mon233
 
2.3%
1yrs 0mon224
 
2.2%
0yrs 8mon220
 
2.2%
0yrs 9mon219
 
2.2%
0yrs 11mon203
 
2.0%
0yrs 5mon200
 
2.0%
1yrs 1mon193
 
1.9%
Other values (102)2931
29.3%

Length

2022-05-15T12:02:34.526512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0yrs7188
35.9%
0mon5467
27.3%
1yrs1584
 
7.9%
2yrs672
 
3.4%
6mon466
 
2.3%
1mon453
 
2.3%
4mon429
 
2.1%
5mon420
 
2.1%
7mon419
 
2.1%
2mon415
 
2.1%
Other values (16)2487
 
12.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CREDIT_HISTORY_LENGTH
Categorical

HIGH CARDINALITY

Distinct187
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0yrs 0mon
5075 
0yrs 6mon
 
215
2yrs 1mon
 
213
0yrs 7mon
 
172
2yrs 0mon
 
157
Other values (182)
4168 

Length

Max length11
Median length9
Mean length9.0894
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.3%

Sample

1st row0yrs 0mon
2nd row0yrs 0mon
3rd row0yrs 5mon
4th row0yrs 0mon
5th row0yrs 0mon

Common Values

ValueCountFrequency (%)
0yrs 0mon5075
50.7%
0yrs 6mon215
 
2.1%
2yrs 1mon213
 
2.1%
0yrs 7mon172
 
1.7%
2yrs 0mon157
 
1.6%
1yrs 0mon150
 
1.5%
1yrs 1mon140
 
1.4%
1yrs 11mon112
 
1.1%
0yrs 8mon109
 
1.1%
1yrs 3mon108
 
1.1%
Other values (177)3549
35.5%

Length

2022-05-15T12:02:34.585662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0yrs6280
31.4%
0mon5582
27.9%
1yrs1201
 
6.0%
2yrs957
 
4.8%
1mon613
 
3.1%
3yrs505
 
2.5%
6mon495
 
2.5%
7mon404
 
2.0%
2mon395
 
2.0%
3mon384
 
1.9%
Other values (23)3184
15.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NO_OF_INQUIRIES
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2133
Minimum0
Maximum18
Zeros8624
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-05-15T12:02:34.644684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7317490387
Coefficient of variation (CV)3.430609652
Kurtosis112.0155896
Mean0.2133
Median Absolute Deviation (MAD)0
Skewness7.899853101
Sum2133
Variance0.5354566557
MonotonicityNot monotonic
2022-05-15T12:02:34.698690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
08624
86.2%
1989
 
9.9%
2236
 
2.4%
370
 
0.7%
437
 
0.4%
518
 
0.2%
610
 
0.1%
84
 
< 0.1%
93
 
< 0.1%
102
 
< 0.1%
Other values (6)7
 
0.1%
ValueCountFrequency (%)
08624
86.2%
1989
 
9.9%
2236
 
2.4%
370
 
0.7%
437
 
0.4%
518
 
0.2%
610
 
0.1%
72
 
< 0.1%
84
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
171
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
84
 
< 0.1%
72
 
< 0.1%
610
0.1%

LOAN_DEFAULT
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
6746 
1
3254 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
06746
67.5%
13254
32.5%

Length

2022-05-15T12:02:34.761714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T12:02:34.798728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
06746
67.5%
13254
32.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-15T12:02:25.824166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:27.194046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:29.410884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:31.475766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:33.679282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:35.723747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:37.825615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:39.865607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:42.050857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:44.096337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:46.229822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:48.205462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:50.341783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:52.341238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:54.370015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:56.469492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:58.570474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:00.758989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:02.785450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:04.816539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:06.920017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:09.021852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:11.104330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:13.184793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:15.386296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:17.482289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:19.575765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:21.645957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:23.765950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:25.894193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:01:27.274064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-15T12:02:02.713433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:04.746523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:06.846000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:08.951837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:11.035314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:13.114533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:15.312279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:17.410273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:19.505749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:21.575941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:23.693933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-15T12:02:25.752137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-15T12:02:34.863766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-15T12:02:35.104044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-15T12:02:35.341135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-15T12:02:35.553743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-15T12:02:35.768792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-15T12:02:28.121119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-15T12:02:28.847063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-15T12:02:29.032123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexUNIQUEIDDISBURSED_AMOUNTASSET_COSTLTVBRANCH_IDSUPPLIER_IDMANUFACTURER_IDCURRENT_PINCODE_IDDATE_OF_BIRTHEMPLOYMENT_TYPEDISBURSAL_DATESTATE_IDEMPLOYEE_CODE_IDMOBILENO_AVL_FLAGAADHAR_FLAGPAN_FLAGVOTERID_FLAGDRIVING_FLAGPASSPORT_FLAGPERFORM_CNS_SCOREPERFORM_CNS_SCORE_DESCRIPTIONPRI_NO_OF_ACCTSPRI_ACTIVE_ACCTSPRI_OVERDUE_ACCTSPRI_CURRENT_BALANCEPRI_SANCTIONED_AMOUNTPRI_DISBURSED_AMOUNTSEC_NO_OF_ACCTSSEC_ACTIVE_ACCTSSEC_OVERDUE_ACCTSSEC_CURRENT_BALANCESEC_SANCTIONED_AMOUNTSEC_DISBURSED_AMOUNTPRIMARY_INSTAL_AMTSEC_INSTAL_AMTNEW_ACCTS_IN_LAST_SIX_MONTHSDELINQUENT_ACCTS_IN_LAST_SIX_MONTHSAVERAGE_ACCT_AGECREDIT_HISTORY_LENGTHNO_OF_INQUIRIESLOAN_DEFAULT
027628573637512806884476.9920140048663071984-03-06Salaried2018-10-1356741100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
1116249539920597137255184.0820181108662791972-05-07Self employed2018-09-26512961100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
2119467615203528186800783.8178188048624501976-01-01Salaried2018-10-2441357110000763B-Very Low Risk11024969990999000000000100yrs 5mon0yrs 5mon00
378738443321483496218880.403418006869971986-01-15Self employed2018-08-17619611100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
45957613497605478300074.5818172924826861988-01-01Self employed2018-10-2441551001000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
58601580160413947593156.631185644561041989-01-01Self employed2018-10-1531237110000365K-High Risk441100338110697861069786000000134340121yrs 3mon2yrs 6mon01
663416621570424847009062.9036185208667521988-07-01Salaried2018-10-251311221011000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
780328574997706679107079.991352200212015961988-05-15Salaried2018-10-1342128111000778B-Very Low Risk64016645593343582330000300000000101yrs 11mon3yrs 7mon00
846055426611547596430387.095143478633531989-06-20Salaried2018-08-0798051100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
9102215050736751712440356.51362404412067241989-07-14Self employed2018-09-14132820101100790B-Very Low Risk33091711100001019500000000200yrs 9mon1yrs 8mon00

Last rows

df_indexUNIQUEIDDISBURSED_AMOUNTASSET_COSTLTVBRANCH_IDSUPPLIER_IDMANUFACTURER_IDCURRENT_PINCODE_IDDATE_OF_BIRTHEMPLOYMENT_TYPEDISBURSAL_DATESTATE_IDEMPLOYEE_CODE_IDMOBILENO_AVL_FLAGAADHAR_FLAGPAN_FLAGVOTERID_FLAGDRIVING_FLAGPASSPORT_FLAGPERFORM_CNS_SCOREPERFORM_CNS_SCORE_DESCRIPTIONPRI_NO_OF_ACCTSPRI_ACTIVE_ACCTSPRI_OVERDUE_ACCTSPRI_CURRENT_BALANCEPRI_SANCTIONED_AMOUNTPRI_DISBURSED_AMOUNTSEC_NO_OF_ACCTSSEC_ACTIVE_ACCTSSEC_OVERDUE_ACCTSSEC_CURRENT_BALANCESEC_SANCTIONED_AMOUNTSEC_DISBURSED_AMOUNTPRIMARY_INSTAL_AMTSEC_INSTAL_AMTNEW_ACCTS_IN_LAST_SIX_MONTHSDELINQUENT_ACCTS_IN_LAST_SIX_MONTHSAVERAGE_ACCT_AGECREDIT_HISTORY_LENGTHNO_OF_INQUIRIESLOAN_DEFAULT
999018960521247701239200077.172238528616511992-04-28Self employed2018-09-2041635110000692E-Low Risk220341268875000849871000000293480101yrs 0mon1yrs 10mon00
999183834484368490497695064.98138141085133421982-10-30Salaried2018-08-3193219111000603H-Medium Risk41149157500005000000000023430121yrs 2mon2yrs 6mon00
9992125569570542443947248662.0820239454962241994-02-23Self employed2018-10-1151482110000836A-Very Low Risk10000000000000002yrs 1mon2yrs 1mon00
999370903461837502536540079.4316220044530361974-01-01Salaried2018-08-24144391100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
9994126431626619519996779582.60103188238669761997-05-03Salaried2018-10-2671238110000825A-Very Low Risk20000000000048060010yrs 6mon0yrs 7mon00
999572798569223586977399881.76105157988611921994-08-01Self employed2018-10-11634141100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
999629833635227507837855865.5382185598648781971-05-25Self employed2018-10-29193344110000783B-Very Low Risk510163830000030000000000000002yrs 1mon3yrs 6mon00
999755089528884535596781381.5577183974523411996-05-21Salaried2018-09-23426421100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01
9998131096271814934910529147.499164504953681992-01-20Salaried2018-10-26322531100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
999990677468415374396359660.5418148784527621983-05-03Self employed2018-08-28416961100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon01